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Professional-Machine-Learning-Engineer認定テキスト

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Google Professional Machine Learning Engineer認定試験では、候補者が監督者や監督なしの学習、深い学習、自然言語処理、コンピュータービジョンなどの機械学習の概念を強く理解する必要があります。この試験では、Apache Spark、Apache Hadoop、Google Bigqueryなどのビッグデータテクノロジーを使用する候補者の能力もテストしています。

Google Professional Machine Learning Engineer認定試験は、Googleクラウドプラットフォームでスケーラブルで効率的な機械学習モデルの設計、構築、展開に習熟したいと考えている機械学習エンジニア、データサイエンティスト、ソフトウェアエンジニアにとって貴重な資格です。認定試験に合格することにより、候補者は仲間と区別し、機械学習の分野での専門知識を実証することができます。

Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題 (Q62-Q67):

質問 # 62
Machine Learning Specialist is training a model to identify the make and model of vehicles in images. The Specialist wants to use transfer learning and an existing model trained on images of general objects. The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.
What should the Specialist do to initialize the model to re-train it with the custom data?

A. Initialize the model with random weights in all layers and replace the last fully connected layer.
B. Initialize the model with pre-trained weights in all layers including the last fully connected layer.
C. Initialize the model with random weights in all layers including the last fully connected layer.
D. Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.

正解：D

解説：
Explanation/Reference:

質問 # 63
Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

A. Cloud Composer, BigQuery ML , and Al Platform Prediction
B. Cloud Composer, Al Platform Training with custom containers , and App Engine
C. Kubeflow Pipelines and App Engine
D. Kubeflow Pipelines and Al Platform Prediction

正解：D

質問 # 64
You have been asked to build a model using a dataset that is stored in a medium-sized (~10 GB) BigQuery table. You need to quickly determine whether this data is suitable for model development. You want to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team. You require maximum flexibility to create your report. What should you do?

A. Use the Google Data Studio to create the report.
B. Use Dataprep to create the report.
C. Use Vertex AI Workbench user-managed notebooks to generate the report.
D. Use the output from TensorFlow Data Validation on Dataflow to generate the report.

正解：D

質問 # 65
You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company's product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use Al Platform's continuous evaluation service to ensure that the models have high accuracy on your test data set. What should you do?

A. Extend your test dataset with images of the newer products when they are introduced to retraining
B. Replace your test dataset with images of the newer products when they are introduced to retraining.
C. Update your test dataset with images of the newer products when your evaluation metrics drop below a pre-decided threshold.
D. Keep the original test dataset unchanged even if newer products are incorporated into retraining

正解：A

質問 # 66
Your company manages a video sharing website where users can watch and upload videos. You need to create an ML model to predict which newly uploaded videos will be the most popular so that those videos can be prioritized on your company's website.
Which result should you use to determine whether the model is successful?

A. The Pearson correlation coefficient between the log-transformed number of views after 7 days and 30 days after publication is equal to 0.
B. The model predicts 95% of the most popular videos measured by watch time within 30 days of being uploaded.
C. The model predicts 97.5% of the most popular clickbait videos measured by number of clicks.
D. The model predicts videos as popular if the user who uploads them has over 10,000 likes.

正解：B

質問 # 67
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